在非匀质成像中,器官形状是影响建模光在生物体内传播过程的重要因素,它能直接影响荧光分子断层成像(FMT)的重建过程。器官图像的手动分割过程较为复杂,且对图像质量要求较高,而边缘检测、区域生长、主动轮廓模型等自动分割方法在处理复杂医学图像时存在很大的局限性。因此,使用基于主动形状模型(ASM)的自动分割方法,对小鼠器官图像进行准确分割,并使用基于L₁范数优化的重建算法实现光源重建。为分析基于ASM的器官图像分割精度与重建精度的关系,采集小鼠计算机断层扫描(CT)数据并进行真实实验,与流行的基于Snake模型的分割算法进行比较。实验结果表明,ASM算法可以替代手动分割,不影响光源的位置重建。

The organ shape is an important factor that affects the propagation of modeling light in vivo. It can directly affect the reconstruction process of fluorescence molecular tomography (FMT). Manual segmentation of organs is complex and requires high-quality images, while automatic segmentation methods such as edge detection, region growing and active contour models have great limitations in dealing with complex medical images. We propose an automatic segmentation method based on active shape models (ASM) to accurately segment the images of mouse organs. Moreover, the light source reconstruction is realized based on L1 norm optimization. We carry out an experiment with the computed tomography (CT) data of a real mouse to explore the relation between organ image segmentation accuracy based on ASM and reconstruction accuracy. The experimental results show that the ASM method can replace manual segmentation without affecting the position reconstruction of light source, when compared with the popular Snake model-based segmentation algorithm.